feat: Add real attention, KV cache, RoPE, and tokenizer to BitNet backend

Resolves the three blocking gaps that prevented end-to-end inference:

1. **Real attention layer** (was pass-through placeholder):
   - AttentionWeights struct with Q/K/V/O ternary projections
   - GQA (Grouped Query Attention) with configurable num_heads / num_kv_heads
   - Pre-computed RoPE cos/sin tables (apply_rope)
   - Per-layer KV cache for autoregressive generation
   - forward_token() for efficient single-token inference with cache
   - forward_layer_cached() with full attention computation
   - forward_layer_nocache() legacy path for backwards compatibility

2. **Tokenizer integration** (was raw bytes → token IDs):
   - load_tokenizer_from_gguf() extracts vocab + merges from GGUF metadata
   - Byte-level fallback tokenizer (260 tokens) when GGUF has no vocab
   - TokenizerBridge implements crate-level Tokenizer trait
   - tok() accessor for direct tokenizer access

3. **generate() uses tokenizer** (was returning [token_id] strings):
   - Encodes prompt via BPE tokenizer before forward pass
   - Decodes generated tokens back to text
   - generate_cached() for KV-cached autoregressive generation
   - get_embeddings() now uses tokenizer for text encoding
   - reset_cache() to clear KV state between sequences

Tests: 174/174 bitnet tests pass (9 new: RoPE, KV cache, tokenizer roundtrip,
attention weights, byte-level fallback, cache operations)

https://claude.ai/code/session_011nTcGcn49b8YKJRVoh4TaK

Author: claude